The recent successes of neural architectures on a number of traditional natural language processing subtasks  [1]}, [2]}, [3]} have motivated investigation into dialogue agents that can effectively make use of distributed neural representations for dialogue state management, belief tracking, and response generation. Recent work by  [4]} has built systems with modularly-connected representation, belief state, and generation components. These models learn to explicitly represent user intent through intermediate supervision, which breaks end-to-end trainability. Other work by  [5]}, [6]} stores dialogue context in a memory module and repeatedly queries and reasons about this context to select an adequate system response from a set of all candidate responses.
